> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
> Use this file to discover all available pages before exploring further.
# OpenLIT Overview
> OpenLIT is an open-source AI Engineering platform for LLM observability, evaluations, prompt management, and cost tracking - built on OpenTelemetry, fully self-hostable.
Understand what your AI agents are doing, measure their performance, and improve them in production.
With OpenLIT teams can Trace LLM and agent calls, compare models, iterate on prompts, detect regressions, and optimize cost and performance using real production data.
LLM observability and AI agent observability for agents built with CrewAI, LangGraph, or any agent framework. OpenLIT brings OpenTelemetry-native instrumentation so you can analyze how your agents perform in production with full stack visibility:
* **Tracing** - Instrument once via [OpenTelemetry-native SDKs](/latest/sdk/overview) for 90+ LLMs and agent frameworks to produce OpenTelemetry traces.
* **Lowest level transparency** - [Exceptions Monitoring](/latest/openlit/observability/telemetry#exceptions) surfaces errors with full stack traces and span context so you know exactly where and why something failed. All telemetry is automatically redacted for sensitive data before it leaves your stack.
* **Understand cost and latency** - Track token consumption, spend per model, provider, and request. Monitor GPU utilization alongside LLM call latency in [custom dashboards](/latest/openlit/dashboards/overview) built on your raw telemetry.
Prompt management, prompt versioning, and model testing tools for teams building AI applications:
* **Prompt versioning and deployment** - [Prompt Hub](/latest/openlit/prompts-experiments/prompt-hub/overview) treats prompts as versioned artifacts. Edit, version, and deploy to any environment without code changes. Roll back instantly when needed.
* **Collaborate on prompts** - Edit prompts as a team with full change history. Every version is tracked and linked to the evaluations and traces it produced, giving you full traceability from prompt to output.
* **Model comparison and A/B testing** - [OpenGround](/latest/openlit/prompts-experiments/openground/overview) runs side-by-side LLM prompt testing across models, comparing cost, latency, and output quality so you pick the right model before you ship.
Coding agent observability for Claude Code, Cursor, and Codex. Understand token consumption, AI spend, and developer productivity across your entire engineering team:
* [One-command setup](/latest/openlit/coding-agents/setup-and-configure) - Install the `openlit` CLI and wire any vendor in under a minute. No SDK, no code changes in your repos.
* **Session monitoring** - Track cost per session, token usage, lines added and removed, model acceptance rate, commits, and PRs per user and per team.
* **AI spend visibility** - See exactly how much each developer, team, and project spends on AI coding tools. Break down cost by model and vendor across Claude Code, Cursor, and Codex in one place.
* **Privacy controls** - Automatic secret redaction, usage breakdowns by repo, and cohort-floor anonymization so individual data stays protected.
LLM evaluation in production and development using the same evaluators. Close the loop between what you ship and what you measure:
* **Online LLM evaluation** - [LLM-as-a-judge](/latest/openlit/evaluations/llm-as-a-judge) runs automatically on your live traces in production. Create custom evaluators with your own scoring criteria and quality gates. No setup required.
* **Offline evaluation and CI/CD testing** - Use the same evaluators via the [programmatic evaluations API](/latest/openlit/evaluations/programmatic-evals) for regression testing in CI pipelines and local development. Catch quality regressions before they reach production.
* **Traceability** - Every evaluation score is linked back to the exact prompt version, model, and trace that produced it so you know what changed and why quality shifted.
Manage the agents, keys, and pricing behind your AI stack from one place:
* **Track and manage agents** - See every agent version running in production, monitor health, and manage deployments from the `/agents` page.
* **Vault** - [Centrally store and rotate](/latest/openlit/developer-resources/vault/overview) LLM API keys that agents retrieve at runtime. No restarts, no code changes, no secrets in your codebase.
* **AI cost tracking** - [Costs](/latest/openlit/costs/overview) under Monitor combines spend analytics, [Manage models](/latest/openlit/costs/manage-models/overview), and Auto Pricing. OpenLIT ships with pricing for popular models and applies it across traces, dashboards, and cost reports.
**Otter** is a built-in AI copilot that works across all of OpenLIT. Query your telemetry, analyze traces, improve prompts, and generate dashboards through natural language:
* [Natural language queries](/latest/openlit/chat/overview) - Ask questions about traces, costs, and tokens. Otter converts them to SQL and runs them inline.
* **Trace and span analysis** - Get structured AI analysis across performance, reliability, cost, token efficiency, and execution path.
* **Prompt improvement** - Ask Otter to review and suggest improvements to any Prompt Hub prompt.
* **Dashboard generation** - Describe the dashboard you want and Otter generates a complete importable layout with working queries.
* **Resource management** - Create rules, contexts, vault secrets, and custom models through conversation.
## Get Started
Production-ready AI Observability in 2 steps with zero code changes
Track spend and usage for Claude Code, Cursor, & more across teams
Self-host the full platform with Docker Compose or Helm
Score live traces automatically with prebuilt LLM-as-a-judge evaluators
## Frequently asked questions
OpenLIT is an open-source AI Engineering platform. It traces LLM and agent calls via OpenTelemetry, tracks cost and token usage, runs automated evaluations, and manages prompts - all self-hostable.
Yes. OpenLIT is fully open source and self-hostable via Docker Compose or Helm, so your telemetry and prompts never have to leave your infrastructure.
No. OpenLIT's SDK auto-instruments 90+ LLMs, agent frameworks, and vector databases with zero code changes, or you can call `openlit.init()` once for manual instrumentation if you want more control - both produce the same OpenTelemetry traces.
OpenLIT combines AI observability, evaluation, cost tracking, and prompt management in one open-source platform, instead of requiring separate tools for tracing, evals, and prompt versioning.